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Telecommunication Systems

, Volume 70, Issue 2, pp 245–262 | Cite as

A Novel HGBBDSA-CTI Approach for Subcarrier Allocation in Heterogeneous Network

  • Mohammad Kamrul HasanEmail author
  • Ahmad Fadzil Ismail
  • Shayla Islam
  • Wahidah Hashim
  • Musse Mohamud Ahmed
  • Imran Memon
Article

Abstract

In recent times, Heterogeneous Network (HetNet) achieves the capacity and coverage for indoors through the deployment of small cells i.e. femtocells (HeNodeBs). These HeNodeBs are plug-and-play Customer Premises Equipment’s which are associated with the internet protocol backhaul to macrocell (macro-eNodeB). The random placement of HeNodeBs deployed in co-channel along with macro-eNodeB is causing severe system performance degradation. Thereby, these HeNodeBs are suggested as the ultimate and the most significant cause of interference in Orthogonal Frequency-Division Multiple-Access based HetNets due to the restricted co-channel deployment. The CTI in such systems can significantly reduce the throughput, and the outages can rise to the unacceptable limit or extremely high levels. These lead to severe system performance degradation in HetNets. This paper presents a novel HGBBDSA-CTI approach capable of strategically allocate the subcarriers and thereby improves the throughput as well as the outage. The enhanced system performance is able to mitigate CTI issues in HetNets. This paper also analyses the time complexity for the proposed HGBBDSA algorithm and also compares it with the Genetic Algorithm-based Dynamic Subcarrier Allocation (DSA), and Particle Swarm Optimization-based DSA as well. The key target of this study is to allocate the unoccupied subcarriers by sharing among the HeNodeBs. The reason is also to enhance the system performance such as throughput of HeNodeB, the average throughput of HeNodeB Users, and outage. The simulation results show that the proposed HGBBDSA-CTI approach enhances the average throughput (92.05 and 74.44%), throughput (30.50 and 74.34%), and the outage rate reduced to 52.9 and 50.76% compare with the existing approaches. The result also indicates that the proposed HGBBDSA approach has less time complexity than the existing approaches.

Keywords

OFDMA resource optimization Computational complexity Subcarrier allocation Co-tier interference Heterogeneous network 

Notes

Acknowledgements

A distinct acknowledgements to Ministry of Higher Education (MOHE), Malaysia for the sponsors. Authors thankfully acknowledge for the support of this work by the Research Management Centre, International Islamic University Malaysia under the Project SF16-003-0072 and Research Management and Innovation Centre, Universiti Malaysia Sarawak under the Grant F02/DPD/1639/2018.

References

  1. 1.
    Hasan, M., Ismail, A. F., Abdalla, A. H., Abdullah, K., Ramli, H., Islam, S., & Saeed, R. (2013). In Inter-cell interference coordination in lte-a hetnets: A survey on self organizing approaches, In 2013 international conference on computing, electrical and electronics engineering (ICCEEE) (pp. 196–201). IEEE.Google Scholar
  2. 2.
    Hasan, M. K., Ismail, A. F., Abdalla, A., Ramli, H., Islam, S., & Hashim, W. (2014) In performance analysis of spectrum sensing methods: A numerical approach, In 2014 international conference on computer and communication engineering (ICCCE) (pp. 193–196). IEEE.Google Scholar
  3. 3.
    Hasan, M. K., Ismail, A. F., Aisha, H., Abdullah, K., Ramli, H., Islam, S., Nafi, N., & Mohamad, H. (2013). In inter-cell interference coordination in heterogeneous network: A qualitative and quantitative analysis, In 2013 IEEE Malaysia international conference on communications (MICC) (pp. 361–366). IEEE.Google Scholar
  4. 4.
    El Ayach, O., Peters, S. W., & Heath, R. W, Jr. (2013). The practical challenges of interference alignment. IEEE Wireless Communications, 20, 35–42.CrossRefGoogle Scholar
  5. 5.
    Bharucha, Z., Haas, H., Saul, A., & Auer, G. (2010). Throughput enhancement through femto-cell deployment. Transactions on Emerging Telecommunications Technologies, 21(5), 469–477.CrossRefGoogle Scholar
  6. 6.
    Cheung, W. C., Quek, T. Q., & Kountouris, M. (2012). Throughput optimization, spectrum allocation, and access control in two-tier femtocell networks. IEEE Journal on Selected Areas in Communications, 30, 561–574.CrossRefGoogle Scholar
  7. 7.
    Marshoud, H., Otrok, H., Barada, H., Estrada, R., Jarray, A., & Dziong, Z. (2012). In resource allocation in macrocell-femtocell network using genetic algorithm, In 2012 IEEE 8th international conference on wireless and mobile computing, networking and communications (WiMob) (pp. 474–479). IEEE.Google Scholar
  8. 8.
    Hasan, M. K., Ismail, A. F., Hashim, W., Islam, S., & Hashim, A. H. (2017). Outage probability analysis of Co-Tier interference in heterogeneous network. Elektronika ir Elektrotechnika, 23(5), 89–93.CrossRefGoogle Scholar
  9. 9.
    Hasan, M. K., Ismail, A. F., Abdalla, A. H., Ramli, H. A., Hashim, W., & Islam, S. (2016). Throughput maximization for the cross-tier interference in heterogeneous network. Advanced Science Letters, 22(10), 2785–9.CrossRefGoogle Scholar
  10. 10.
    Başaran, S. T., & Kurt, G. K. (2016). Joint subcarrier and power allocation in OFDMA systems for outage minimization. IEEE Communications Letters, 20(10), 2007–10.CrossRefGoogle Scholar
  11. 11.
    Zhang, H., Liu, H., Cheng, J., & Leung, V. C. (2017). Downlink energy efficiency of power allocation and wireless backhaul bandwidth allocation in heterogeneous small cell networks. IEEE Transactions on Communications, 66(4), 1705–1716.CrossRefGoogle Scholar
  12. 12.
    Zhang, Q., Fu, B., Feng, Z., & Li, W. (2017). Utility-maximized two-level game-theoretic approach for bandwidth allocation in heterogeneous radio access networks. IEEE Transactions on Vehicular Technology., 66(1), 844–54.CrossRefGoogle Scholar
  13. 13.
    Bai, B., Chen, W., Cao, Z., & Letaief, K. B. (2008). Achieving high frequency diversity with subcarrier allocation in OFDMA systems. In IEEE GLOBECOM 2008 global telecommunications conference, 2008 (pp. 1–5). IEEE.Google Scholar
  14. 14.
    Li, Z., Guo, S., Li, W., Lu, S., Chen, D., & Leung, V. (2012). A particle swarm optimization algorithm for resource allocation in femtocell networks, 2012 IEEE conference on wireless communications and networking (WCNC) (pp. 1212–1217). IEEE.Google Scholar
  15. 15.
    Modrak, V., & Marton, D. (2013). Development of metrics and a complexity scale for the topology of assembly supply chains. Entropy, 15, 4285–4299.CrossRefGoogle Scholar
  16. 16.
    Febres, G., & Jaffe, K. (2015). A fundamental scale of descriptions for analyzing information content of communication systems. Entropy, 17, 1606–1633.CrossRefGoogle Scholar
  17. 17.
    Chang, Y.-C., Wu, H.-T., Chen, H.-R., Liu, A.-B., Yeh, J.-J., Lo, M.-T., et al. (2014). Application of a modified entropy computational method in assessing the complexity of pulse wave velocity signals in healthy and diabetic subjects. Entropy, 16, 4032–4043.CrossRefGoogle Scholar
  18. 18.
    Mohjazi, L., Al-Qutayri, M., Barada, H., Poon, K. F. (2011). Performance evaluation of heuristic techniques for coverage optimization in femtocells, 2011 18th IEEE international conference on electronics, circuits and systems (ICECS) (pp. 587–590). IEEE.Google Scholar
  19. 19.
    Shahid, A., Aslam, S., & Lee, K.-G. (2013). A decentralized heuristic approach towards resource allocation in femtocell networks. Entropy, 15, 2524–2547.CrossRefGoogle Scholar
  20. 20.
    Obaidat, M. S., Zarai, F., & Nicopolitidis, P. (2015). Modeling and simulation of computer networks and systems: Methodologies and applications. Burlington: Morgan Kaufmann, Elsevier.Google Scholar
  21. 21.
    Odhah, N. A., Dessouky, M. I., Al-Hanafy, W. E., & Abd El-Samie, F. (2012). Low complexity greedy power allocation algorithm for proportional resource allocation in multi-user ofdm systems. Journal of Telecommunications and Information Technology, 1(4), 38–45.Google Scholar
  22. 22.
    Shannon, C. E., Weaver, W., & Burks, A. W. (1951). The mathematical theory of communication. Philosophical Review, 60(3), 398–400.  https://doi.org/10.2307/2181879.CrossRefGoogle Scholar
  23. 23.
    Wang, X., & Duan, H. (2013). Predator-prey biogeography-based optimization for bio-inspired visual attention. International Journal of Computational Intelligence Systems, 6, 1151–1162.CrossRefGoogle Scholar
  24. 24.
    Ma, H., & Simon, D. (2011). Blended biogeography-based optimization for constrained optimization. Engineering Applications of Artificial Intelligence, 24, 517–525.CrossRefGoogle Scholar
  25. 25.
    Ikuno, J. C., Wrulich, M., & Rupp, M. (2010). System level simulation of lte networks, 71st IEEE vehicular technology conference (VTC 2010-Spring) (pp. 1–5). IEEE.Google Scholar
  26. 26.
    Simsek, M., Akbudak, T., Zhao, B., & Czylwik, A. (2010). An lte-femtocell dynamic system level simulator, Smart Antennas (WSA). Workshop on International ITG (pp. 66–71).Google Scholar
  27. 27.
    Akyildiz, I. F., Gutierrez-Estevez, D. M., & Reyes, E. C. (2010). The evolution to 4g cellular systems: Lte-advanced. Physical Communication, 3, 217–244.CrossRefGoogle Scholar
  28. 28.
    Dahlman, E., Parkvall, S., & Skold, J. (2013). 4g: Lte/lte-advanced for mobile broadband. New York: Academic Press, Elsevier.Google Scholar
  29. 29.
    Yuan, G., Zhang, X., Wang, W., & Yang, Y. (2010). Carrier aggregation for lte-advanced mobile communication systems. IEEE Communications Magazine, 48, 88–93.CrossRefGoogle Scholar
  30. 30.
    Zeng, L., McGrath, S. (2012). Joint spectrum sensing and power allocation algorithm for spectrum efficiency optimization in ultra wideband cognitive radio networks, IEEE conference on vehicular technology conference (VTC Fall) (pp. 1–5). IEEE.Google Scholar
  31. 31.
    Khandekar, A., Bhushan, N., Tingfang, J., & Vanghi, V. (2010). Lte-advanced: Heterogeneous networks, 2010 European wireless conference (EW) (pp 978–982). IEEE.Google Scholar
  32. 32.
    Cox, C. (2012). An introduction to lte: Lte, lte-advanced, sae and 4g mobile communications. Hoboken: Wiley.CrossRefGoogle Scholar
  33. 33.
    Wang, X., & Giannakis, G. B. (2011). Resource allocation for wireless multiuser ofdm networks. IEEE Transactions on Information Theory, 57, 4359–4372.CrossRefGoogle Scholar
  34. 34.
    Martín-Sacristán, D., Monserrat, J. F., Cabrejas-Penuelas, J., Calabuig, D., Garrigas, S., & Cardona, N. (2009). On the way towards fourth-generation mobile: 3GPP LTE and LTE-advanced. EURASIP Journal on Wireless Communications and Networking, 1(2009), 354089.CrossRefGoogle Scholar
  35. 35.
    Access, E.U.T.R. User equipment (ue) radio transmission and reception, 3gpp std. Ts 36.101.Google Scholar
  36. 36.
    Access, E.U.T.R. Further advancements for e-utra physical layer aspects, 3gpp ts 36.814. V9. 0.0, Mar (2010)Google Scholar
  37. 37.
    LTE, E. Evolved universal terrestrial radio access (e-utra); base station (bs) radio transmission and reception (3gpp ts 36.104 version 8.6. 0 release 8). ETSI TS, \(136\), V8 (2009).Google Scholar
  38. 38.
    Bouras, C., Kokkinos, V., Papazois, A., & Tseliou, G. (2013). Fractional frequency reuse in integrated femtocell/macrocell environments,In WWIC, 2013 (pp. 229–240). Springer, New York.Google Scholar
  39. 39.
    Boussaid, I., Chatterjee, A., Siarry, P., & Ahmed-Nacer, M. (2011). Hybridizing biogeography-based optimization with differential evolution for optimal power allocation in wireless sensor networks. IEEE Transactions on Vehicular Technology, 60, 2347–2353.CrossRefGoogle Scholar
  40. 40.
    Oh, D.-C., & Lee, Y.-H. (2012). Cognitive radio based resource allocation in femto-cells. Journal of Communications and Networks, 14, 252–256.CrossRefGoogle Scholar
  41. 41.
    3GPPTS32.500V10.1.0. Universal mobile telecommunications system (umts);lte; telecommunication management; self-organizing networks (son); concepts andrequirements, release 10,3GPP-ETSI: (2011).Google Scholar
  42. 42.
    Han, S., et al. (2016). Hierarchical-game-based algorithm for downlink joint subchannel and power allocation in OFDMA femtocell networks. Journal of Network and Computer Applications, 73, 44–56.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical and and Electronics EngineeringUniversiti Malaysia Sarawak (UNIMAS)Kota SamarahanMalaysia
  2. 2.Department of Electrical and Computer EngineeringInternational Islamic University MalaysiaKuala LumpurMalaysia
  3. 3.Department of Computer Science and EngineeringGreen University of BangladeshDhakaBangladesh
  4. 4.Institute of Informatics and Computing in EnergyUniversiti Tenaga Nasional (UNITEN)KajangMalaysia
  5. 5.College of Computer ScienceZhejiang UniversityHangzhouChina

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